Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.8 KiB
Average record size in memory168.0 B

Variable types

Text3
Numeric15
Categorical2

Alerts

Acousticness is highly overall correlated with EnergyHigh correlation
Danceability is highly overall correlated with ValenceHigh correlation
Energy is highly overall correlated with Acousticness and 1 other fieldsHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Track_Length is highly overall correlated with Track_Word_LengthHigh correlation
Track_Word_Length is highly overall correlated with Track_LengthHigh correlation
Valence is highly overall correlated with DanceabilityHigh correlation
Time_Signature is highly imbalanced (82.8%)Imbalance
Key has 136 (14.0%) zerosZeros
Instrumentalness has 282 (29.1%) zerosZeros
Popularity has 13 (1.3%) zerosZeros

Reproduction

Analysis started2024-09-15 19:35:53.808957
Analysis finished2024-09-15 19:36:38.884866
Duration45.08 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Track
Text

Distinct953
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:39.206205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length66
Median length47
Mean length18.166322
Min length2

Characters and Unicode

Total characters17585
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique938 ?
Unique (%)96.9%

Sample

1st rowabc
2nd rowlet it be
3rd rowi want you back
4th rowcecilia
5th rowspirit in the sky
ValueCountFrequency (%)
the 157
 
4.4%
you 126
 
3.5%
love 114
 
3.2%
to 79
 
2.2%
me 75
 
2.1%
i 70
 
2.0%
in 63
 
1.8%
a 52
 
1.5%
my 48
 
1.4%
of 44
 
1.2%
Other values (1118) 2726
76.7%
2024-09-15T16:36:39.704112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2586
14.7%
e 1755
 
10.0%
o 1424
 
8.1%
t 1119
 
6.4%
n 1076
 
6.1%
a 1041
 
5.9%
i 998
 
5.7%
l 784
 
4.5%
r 699
 
4.0%
s 661
 
3.8%
Other values (43) 5442
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2586
14.7%
e 1755
 
10.0%
o 1424
 
8.1%
t 1119
 
6.4%
n 1076
 
6.1%
a 1041
 
5.9%
i 998
 
5.7%
l 784
 
4.5%
r 699
 
4.0%
s 661
 
3.8%
Other values (43) 5442
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2586
14.7%
e 1755
 
10.0%
o 1424
 
8.1%
t 1119
 
6.4%
n 1076
 
6.1%
a 1041
 
5.9%
i 998
 
5.7%
l 784
 
4.5%
r 699
 
4.0%
s 661
 
3.8%
Other values (43) 5442
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2586
14.7%
e 1755
 
10.0%
o 1424
 
8.1%
t 1119
 
6.4%
n 1076
 
6.1%
a 1041
 
5.9%
i 998
 
5.7%
l 784
 
4.5%
r 699
 
4.0%
s 661
 
3.8%
Other values (43) 5442
30.9%

Artist
Text

Distinct525
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:40.011833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length51
Median length36
Mean length13.581612
Min length1

Characters and Unicode

Total characters13147
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)35.7%

Sample

1st rowThe Jackson 5
2nd rowThe Beatles
3rd rowThe Jackson 5
4th rowSimon & Garfunkel
5th rowNorman Greenbaum
ValueCountFrequency (%)
the 167
 
7.3%
94
 
4.1%
john 38
 
1.7%
band 33
 
1.5%
and 27
 
1.2%
paul 23
 
1.0%
elton 16
 
0.7%
barry 15
 
0.7%
simon 15
 
0.7%
jackson 14
 
0.6%
Other values (783) 1831
80.6%
2024-09-15T16:36:40.458078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1305
 
9.9%
e 1281
 
9.7%
a 914
 
7.0%
n 909
 
6.9%
r 783
 
6.0%
o 774
 
5.9%
i 716
 
5.4%
t 571
 
4.3%
l 550
 
4.2%
s 506
 
3.8%
Other values (57) 4838
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1305
 
9.9%
e 1281
 
9.7%
a 914
 
7.0%
n 909
 
6.9%
r 783
 
6.0%
o 774
 
5.9%
i 716
 
5.4%
t 571
 
4.3%
l 550
 
4.2%
s 506
 
3.8%
Other values (57) 4838
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1305
 
9.9%
e 1281
 
9.7%
a 914
 
7.0%
n 909
 
6.9%
r 783
 
6.0%
o 774
 
5.9%
i 716
 
5.4%
t 571
 
4.3%
l 550
 
4.2%
s 506
 
3.8%
Other values (57) 4838
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1305
 
9.9%
e 1281
 
9.7%
a 914
 
7.0%
n 909
 
6.9%
r 783
 
6.0%
o 774
 
5.9%
i 716
 
5.4%
t 571
 
4.3%
l 550
 
4.2%
s 506
 
3.8%
Other values (57) 4838
36.8%

Duration
Real number (ℝ)

Distinct248
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.09194
Minimum76
Maximum1008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:40.594058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile150
Q1184
median215
Q3251
95-th percentile353.65
Maximum1008
Range932
Interquartile range (IQR)67

Descriptive statistics

Standard deviation71.201992
Coefficient of variation (CV)0.31353817
Kurtosis19.189715
Mean227.09194
Median Absolute Deviation (MAD)33
Skewness2.8753562
Sum219825
Variance5069.7237
MonotonicityNot monotonic
2024-09-15T16:36:40.717775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 17
 
1.8%
174 13
 
1.3%
233 13
 
1.3%
212 12
 
1.2%
208 12
 
1.2%
213 12
 
1.2%
215 12
 
1.2%
203 11
 
1.1%
227 11
 
1.1%
230 10
 
1.0%
Other values (238) 845
87.3%
ValueCountFrequency (%)
76 1
0.1%
77 1
0.1%
80 1
0.1%
87 2
0.2%
91 1
0.1%
96 1
0.1%
99 1
0.1%
115 1
0.1%
116 1
0.1%
118 1
0.1%
ValueCountFrequency (%)
1008 1
0.1%
646 1
0.1%
645 1
0.1%
582 1
0.1%
539 1
0.1%
523 1
0.1%
516 1
0.1%
501 1
0.1%
491 1
0.1%
483 1
0.1%

Time_Signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
4
913 
3
 
50
1
 
3
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters968
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 913
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Length

2024-09-15T16:36:40.828315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T16:36:40.907080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 913
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 913
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 913
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 913
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 913
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct491
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58894855
Minimum0.0942
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:41.004135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0942
5-th percentile0.304
Q10.48875
median0.6005
Q30.69825
95-th percentile0.82465
Maximum0.985
Range0.8908
Interquartile range (IQR)0.2095

Descriptive statistics

Standard deviation0.15745309
Coefficient of variation (CV)0.26734608
Kurtosis-0.26945468
Mean0.58894855
Median Absolute Deviation (MAD)0.103
Skewness-0.34193101
Sum570.1022
Variance0.024791474
MonotonicityNot monotonic
2024-09-15T16:36:41.117116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.637 9
 
0.9%
0.68 7
 
0.7%
0.665 7
 
0.7%
0.639 6
 
0.6%
0.669 6
 
0.6%
0.541 6
 
0.6%
0.565 6
 
0.6%
0.649 6
 
0.6%
0.671 6
 
0.6%
0.529 5
 
0.5%
Other values (481) 904
93.4%
ValueCountFrequency (%)
0.0942 1
0.1%
0.149 2
0.2%
0.16 1
0.1%
0.164 1
0.1%
0.185 1
0.1%
0.195 1
0.1%
0.203 1
0.1%
0.205 1
0.1%
0.207 1
0.1%
0.212 1
0.1%
ValueCountFrequency (%)
0.985 1
 
0.1%
0.965 1
 
0.1%
0.946 1
 
0.1%
0.925 1
 
0.1%
0.919 1
 
0.1%
0.912 3
0.3%
0.911 2
0.2%
0.908 1
 
0.1%
0.9 1
 
0.1%
0.889 1
 
0.1%

Energy
Real number (ℝ)

HIGH CORRELATION 

Distinct538
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5810682
Minimum0.00532
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:41.229970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00532
5-th percentile0.2497
Q10.4295
median0.583
Q30.73125
95-th percentile0.90565
Maximum0.995
Range0.98968
Interquartile range (IQR)0.30175

Descriptive statistics

Standard deviation0.20184424
Coefficient of variation (CV)0.34736755
Kurtosis-0.57048414
Mean0.5810682
Median Absolute Deviation (MAD)0.151
Skewness-0.14469138
Sum562.47402
Variance0.040741097
MonotonicityNot monotonic
2024-09-15T16:36:41.345703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.673 7
 
0.7%
0.528 7
 
0.7%
0.641 6
 
0.6%
0.644 6
 
0.6%
0.409 5
 
0.5%
0.56 5
 
0.5%
0.532 5
 
0.5%
0.708 4
 
0.4%
0.48 4
 
0.4%
0.492 4
 
0.4%
Other values (528) 915
94.5%
ValueCountFrequency (%)
0.00532 1
0.1%
0.0088 1
0.1%
0.0264 1
0.1%
0.0265 1
0.1%
0.0751 1
0.1%
0.0803 1
0.1%
0.0809 1
0.1%
0.0897 1
0.1%
0.112 1
0.1%
0.116 1
0.1%
ValueCountFrequency (%)
0.995 2
0.2%
0.989 1
0.1%
0.987 1
0.1%
0.98 1
0.1%
0.979 1
0.1%
0.974 1
0.1%
0.969 1
0.1%
0.968 2
0.2%
0.961 1
0.1%
0.957 1
0.1%

Key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2066116
Minimum0
Maximum11
Zeros136
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:41.448102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5717896
Coefficient of variation (CV)0.68601039
Kurtosis-1.2943979
Mean5.2066116
Median Absolute Deviation (MAD)3
Skewness-0.016583793
Sum5040
Variance12.757681
MonotonicityNot monotonic
2024-09-15T16:36:41.538702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 136
14.0%
7 118
12.2%
9 115
11.9%
2 100
10.3%
5 95
9.8%
4 80
8.3%
1 73
7.5%
10 64
6.6%
11 64
6.6%
8 52
 
5.4%
Other values (2) 71
7.3%
ValueCountFrequency (%)
0 136
14.0%
1 73
7.5%
2 100
10.3%
3 25
 
2.6%
4 80
8.3%
5 95
9.8%
6 46
 
4.8%
7 118
12.2%
8 52
 
5.4%
9 115
11.9%
ValueCountFrequency (%)
11 64
6.6%
10 64
6.6%
9 115
11.9%
8 52
5.4%
7 118
12.2%
6 46
 
4.8%
5 95
9.8%
4 80
8.3%
3 25
 
2.6%
2 100
10.3%

Loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct906
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.8718998
Minimum-31.646
Maximum-2.34
Zeros0
Zeros (%)0.0%
Negative968
Negative (%)100.0%
Memory size15.1 KiB
2024-09-15T16:36:41.644543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-31.646
5-th percentile-15.7269
Q1-12.3585
median-9.5625
Q3-7.108
95-th percentile-4.6876
Maximum-2.34
Range29.306
Interquartile range (IQR)5.2505

Descriptive statistics

Standard deviation3.7135008
Coefficient of variation (CV)-0.37616881
Kurtosis2.7129387
Mean-9.8718998
Median Absolute Deviation (MAD)2.586
Skewness-0.9458175
Sum-9555.999
Variance13.790088
MonotonicityNot monotonic
2024-09-15T16:36:41.776655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.472 3
 
0.3%
-10.834 2
 
0.2%
-13.119 2
 
0.2%
-4.653 2
 
0.2%
-9.138 2
 
0.2%
-7.246 2
 
0.2%
-12.923 2
 
0.2%
-10.518 2
 
0.2%
-8.752 2
 
0.2%
-8.555 2
 
0.2%
Other values (896) 947
97.8%
ValueCountFrequency (%)
-31.646 1
0.1%
-30 1
0.1%
-27.103 1
0.1%
-27.09 1
0.1%
-26.128 1
0.1%
-23.56 1
0.1%
-21.657 1
0.1%
-21.644 1
0.1%
-20.518 1
0.1%
-20.439 1
0.1%
ValueCountFrequency (%)
-2.34 1
0.1%
-2.515 1
0.1%
-2.588 1
0.1%
-2.621 1
0.1%
-2.785 1
0.1%
-3.081 1
0.1%
-3.144 1
0.1%
-3.222 1
0.1%
-3.226 1
0.1%
-3.471 1
0.1%

Mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
1
735 
0
233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters968
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 735
75.9%
0 233
 
24.1%

Length

2024-09-15T16:36:41.905885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T16:36:41.997119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 735
75.9%
0 233
 
24.1%

Most occurring characters

ValueCountFrequency (%)
1 735
75.9%
0 233
 
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 233
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 233
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 233
 
24.1%

Speechiness
Real number (ℝ)

Distinct453
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.060116012
Minimum0.0232
Maximum0.737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:42.637046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.026735
Q10.0313
median0.03835
Q30.05685
95-th percentile0.18065
Maximum0.737
Range0.7138
Interquartile range (IQR)0.02555

Descriptive statistics

Standard deviation0.065870831
Coefficient of variation (CV)1.0957285
Kurtosis25.283659
Mean0.060116012
Median Absolute Deviation (MAD)0.00955
Skewness4.3821422
Sum58.1923
Variance0.0043389664
MonotonicityNot monotonic
2024-09-15T16:36:42.796368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0336 9
 
0.9%
0.0346 9
 
0.9%
0.0302 8
 
0.8%
0.0287 8
 
0.8%
0.0283 8
 
0.8%
0.0341 8
 
0.8%
0.0369 7
 
0.7%
0.0282 7
 
0.7%
0.0325 7
 
0.7%
0.0298 7
 
0.7%
Other values (443) 890
91.9%
ValueCountFrequency (%)
0.0232 1
 
0.1%
0.0239 1
 
0.1%
0.024 2
0.2%
0.0241 1
 
0.1%
0.0243 2
0.2%
0.0245 1
 
0.1%
0.0246 2
0.2%
0.0247 1
 
0.1%
0.0248 4
0.4%
0.0249 3
0.3%
ValueCountFrequency (%)
0.737 1
0.1%
0.576 1
0.1%
0.467 1
0.1%
0.457 1
0.1%
0.452 1
0.1%
0.448 1
0.1%
0.405 2
0.2%
0.368 1
0.1%
0.364 1
0.1%
0.361 1
0.1%

Acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct711
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33346865
Minimum2.23 × 10-5
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:42.933835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.23 × 10-5
5-th percentile0.005995
Q10.079525
median0.2715
Q30.54325
95-th percentile0.8596
Maximum0.996
Range0.9959777
Interquartile range (IQR)0.463725

Descriptive statistics

Standard deviation0.27992366
Coefficient of variation (CV)0.83943023
Kurtosis-0.83900503
Mean0.33346865
Median Absolute Deviation (MAD)0.21485
Skewness0.59414452
Sum322.79765
Variance0.078357256
MonotonicityNot monotonic
2024-09-15T16:36:43.064455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.357 8
 
0.8%
0.309 5
 
0.5%
0.305 5
 
0.5%
0.484 5
 
0.5%
0.181 5
 
0.5%
0.0185 4
 
0.4%
0.22 4
 
0.4%
0.524 4
 
0.4%
0.122 4
 
0.4%
0.81 4
 
0.4%
Other values (701) 920
95.0%
ValueCountFrequency (%)
2.23 × 10-51
0.1%
0.000109 1
0.1%
0.000133 1
0.1%
0.000215 1
0.1%
0.000261 1
0.1%
0.00028 1
0.1%
0.000288 1
0.1%
0.000385 1
0.1%
0.000598 1
0.1%
0.000668 2
0.2%
ValueCountFrequency (%)
0.996 1
0.1%
0.994 1
0.1%
0.992 1
0.1%
0.983 1
0.1%
0.973 1
0.1%
0.971 1
0.1%
0.965 1
0.1%
0.959 1
0.1%
0.953 1
0.1%
0.95 1
0.1%

Instrumentalness
Real number (ℝ)

ZEROS 

Distinct608
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046306851
Minimum0
Maximum0.97
Zeros282
Zeros (%)29.1%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:43.199890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.025 × 10-5
Q30.0027275
95-th percentile0.31065
Maximum0.97
Range0.97
Interquartile range (IQR)0.0027275

Descriptive statistics

Standard deviation0.16232817
Coefficient of variation (CV)3.5054894
Kurtosis18.107003
Mean0.046306851
Median Absolute Deviation (MAD)5.025 × 10-5
Skewness4.2835767
Sum44.825031
Variance0.026350436
MonotonicityNot monotonic
2024-09-15T16:36:43.336128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 282
29.1%
0.000122 4
 
0.4%
1.81 × 10-64
 
0.4%
0.00031 3
 
0.3%
0.00141 3
 
0.3%
0.000171 3
 
0.3%
0.00014 3
 
0.3%
1.1 × 10-62
 
0.2%
0.00192 2
 
0.2%
1.68 × 10-62
 
0.2%
Other values (598) 660
68.2%
ValueCountFrequency (%)
0 282
29.1%
1 × 10-61
 
0.1%
1.08 × 10-61
 
0.1%
1.09 × 10-61
 
0.1%
1.1 × 10-62
 
0.2%
1.2 × 10-61
 
0.1%
1.22 × 10-61
 
0.1%
1.23 × 10-61
 
0.1%
1.28 × 10-61
 
0.1%
1.31 × 10-62
 
0.2%
ValueCountFrequency (%)
0.97 1
0.1%
0.968 1
0.1%
0.963 1
0.1%
0.959 2
0.2%
0.944 1
0.1%
0.94 2
0.2%
0.92 1
0.1%
0.916 1
0.1%
0.912 1
0.1%
0.909 1
0.1%

Liveness
Real number (ℝ)

Distinct527
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17544432
Minimum0.015
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:43.470846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.015
5-th percentile0.048075
Q10.0863
median0.119
Q30.19725
95-th percentile0.54125
Maximum0.985
Range0.97
Interquartile range (IQR)0.11095

Descriptive statistics

Standard deviation0.15428562
Coefficient of variation (CV)0.87939936
Kurtosis6.364303
Mean0.17544432
Median Absolute Deviation (MAD)0.0433
Skewness2.3714931
Sum169.8301
Variance0.023804053
MonotonicityNot monotonic
2024-09-15T16:36:43.616348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.105 12
 
1.2%
0.108 11
 
1.1%
0.103 10
 
1.0%
0.113 10
 
1.0%
0.122 9
 
0.9%
0.115 9
 
0.9%
0.12 9
 
0.9%
0.134 8
 
0.8%
0.109 8
 
0.8%
0.104 8
 
0.8%
Other values (517) 874
90.3%
ValueCountFrequency (%)
0.015 1
0.1%
0.0166 1
0.1%
0.0188 1
0.1%
0.0199 1
0.1%
0.0295 2
0.2%
0.0309 1
0.1%
0.0318 1
0.1%
0.032 1
0.1%
0.0339 1
0.1%
0.034 1
0.1%
ValueCountFrequency (%)
0.985 1
0.1%
0.974 1
0.1%
0.962 1
0.1%
0.957 1
0.1%
0.935 1
0.1%
0.9 1
0.1%
0.892 1
0.1%
0.805 1
0.1%
0.792 1
0.1%
0.779 1
0.1%

Valence
Real number (ℝ)

HIGH CORRELATION 

Distinct567
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62309639
Minimum1 × 10-5
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:43.744137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.1787
Q10.42375
median0.6525
Q30.846
95-th percentile0.962
Maximum0.989
Range0.98899
Interquartile range (IQR)0.42225

Descriptive statistics

Standard deviation0.25171319
Coefficient of variation (CV)0.4039715
Kurtosis-0.94357252
Mean0.62309639
Median Absolute Deviation (MAD)0.206
Skewness-0.39979971
Sum603.15731
Variance0.063359528
MonotonicityNot monotonic
2024-09-15T16:36:43.873152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.962 9
 
0.9%
0.963 8
 
0.8%
0.971 6
 
0.6%
0.969 6
 
0.6%
0.926 5
 
0.5%
0.826 5
 
0.5%
0.961 5
 
0.5%
0.967 5
 
0.5%
0.718 5
 
0.5%
0.853 4
 
0.4%
Other values (557) 910
94.0%
ValueCountFrequency (%)
1 × 10-51
0.1%
0.0346 1
0.1%
0.0348 1
0.1%
0.0385 1
0.1%
0.0393 1
0.1%
0.0397 1
0.1%
0.0558 1
0.1%
0.0589 1
0.1%
0.0685 2
0.2%
0.0791 1
0.1%
ValueCountFrequency (%)
0.989 1
 
0.1%
0.985 1
 
0.1%
0.981 1
 
0.1%
0.979 1
 
0.1%
0.978 1
 
0.1%
0.973 1
 
0.1%
0.972 1
 
0.1%
0.971 6
0.6%
0.97 2
 
0.2%
0.969 6
0.6%

Tempo
Real number (ℝ)

Distinct943
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.88992
Minimum53.986
Maximum211.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:43.991891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53.986
5-th percentile79.1578
Q199.972
median117.4365
Q3134.00775
95-th percentile170.93015
Maximum211.27
Range157.284
Interquartile range (IQR)34.03575

Descriptive statistics

Standard deviation27.045684
Coefficient of variation (CV)0.2274851
Kurtosis0.39278855
Mean118.88992
Median Absolute Deviation (MAD)17.1265
Skewness0.58390327
Sum115085.44
Variance731.46901
MonotonicityNot monotonic
2024-09-15T16:36:44.111338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.977 3
 
0.3%
120.157 2
 
0.2%
166.139 2
 
0.2%
132.642 2
 
0.2%
95.048 2
 
0.2%
130.166 2
 
0.2%
85.126 2
 
0.2%
79.764 2
 
0.2%
103.01 2
 
0.2%
107.383 2
 
0.2%
Other values (933) 947
97.8%
ValueCountFrequency (%)
53.986 1
0.1%
61.53 1
0.1%
62.204 1
0.1%
63.059 1
0.1%
65.09 1
0.1%
65.832 1
0.1%
65.861 1
0.1%
67.006 1
0.1%
68.482 1
0.1%
68.69 1
0.1%
ValueCountFrequency (%)
211.27 1
0.1%
207.266 1
0.1%
205.845 1
0.1%
205.747 1
0.1%
203.812 1
0.1%
202.297 1
0.1%
202.14 1
0.1%
201.467 1
0.1%
200.813 1
0.1%
200.423 1
0.1%

Popularity
Real number (ℝ)

ZEROS 

Distinct87
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.630165
Minimum0
Maximum90
Zeros13
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:44.249028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q144
median56
Q367
95-th percentile78
Maximum90
Range90
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.997231
Coefficient of variation (CV)0.33558037
Kurtosis0.60849401
Mean53.630165
Median Absolute Deviation (MAD)11
Skewness-0.8177238
Sum51914
Variance323.90031
MonotonicityNot monotonic
2024-09-15T16:36:44.373302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 31
 
3.2%
64 31
 
3.2%
55 30
 
3.1%
49 26
 
2.7%
74 26
 
2.7%
51 25
 
2.6%
71 25
 
2.6%
67 25
 
2.6%
47 24
 
2.5%
62 24
 
2.5%
Other values (77) 701
72.4%
ValueCountFrequency (%)
0 13
1.3%
1 2
 
0.2%
2 2
 
0.2%
3 1
 
0.1%
4 3
 
0.3%
5 2
 
0.2%
6 2
 
0.2%
7 4
 
0.4%
8 1
 
0.1%
9 2
 
0.2%
ValueCountFrequency (%)
90 2
 
0.2%
89 1
 
0.1%
86 3
 
0.3%
85 3
 
0.3%
84 5
0.5%
83 6
0.6%
82 4
 
0.4%
81 9
0.9%
80 10
1.0%
79 4
 
0.4%

Year
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.5713
Minimum1970
Maximum1979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:44.474348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1970
Q11972
median1975
Q31977
95-th percentile1979
Maximum1979
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8464825
Coefficient of variation (CV)0.0014415699
Kurtosis-1.2078404
Mean1974.5713
Median Absolute Deviation (MAD)2
Skewness-0.021547913
Sum1911385
Variance8.1024629
MonotonicityIncreasing
2024-09-15T16:36:44.581163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1973 100
10.3%
1978 99
10.2%
1977 99
10.2%
1976 99
10.2%
1974 98
10.1%
1979 98
10.1%
1975 97
10.0%
1972 97
10.0%
1971 93
9.6%
1970 88
9.1%
ValueCountFrequency (%)
1970 88
9.1%
1971 93
9.6%
1972 97
10.0%
1973 100
10.3%
1974 98
10.1%
1975 97
10.0%
1976 99
10.2%
1977 99
10.2%
1978 99
10.2%
1979 98
10.1%
ValueCountFrequency (%)
1979 98
10.1%
1978 99
10.2%
1977 99
10.2%
1976 99
10.2%
1975 97
10.0%
1974 98
10.1%
1973 100
10.3%
1972 97
10.0%
1971 93
9.6%
1970 88
9.1%

Track_Length
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.166322
Minimum2
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:44.696290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q112
median16
Q323
95-th percentile36
Maximum66
Range64
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.406824
Coefficient of variation (CV)0.51781664
Kurtosis2.5238125
Mean18.166322
Median Absolute Deviation (MAD)5
Skewness1.2732516
Sum17585
Variance88.488337
MonotonicityNot monotonic
2024-09-15T16:36:44.832195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 58
 
6.0%
13 57
 
5.9%
12 55
 
5.7%
15 54
 
5.6%
19 43
 
4.4%
11 43
 
4.4%
18 42
 
4.3%
16 42
 
4.3%
14 41
 
4.2%
17 38
 
3.9%
Other values (46) 495
51.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 7
 
0.7%
4 15
 
1.5%
5 10
 
1.0%
6 11
 
1.1%
7 30
3.1%
8 34
3.5%
9 32
3.3%
10 58
6.0%
11 43
4.4%
ValueCountFrequency (%)
66 1
 
0.1%
62 1
 
0.1%
60 1
 
0.1%
56 3
0.3%
55 1
 
0.1%
54 1
 
0.1%
52 1
 
0.1%
50 1
 
0.1%
49 1
 
0.1%
48 1
 
0.1%

Track_Word_Length
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6714876
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:44.936929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9915886
Coefficient of variation (CV)0.54244731
Kurtosis1.7547182
Mean3.6714876
Median Absolute Deviation (MAD)1
Skewness1.0757387
Sum3554
Variance3.9664251
MonotonicityNot monotonic
2024-09-15T16:36:45.048540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 234
24.2%
4 184
19.0%
3 180
18.6%
5 105
10.8%
1 94
9.7%
6 91
 
9.4%
7 43
 
4.4%
8 15
 
1.5%
9 10
 
1.0%
10 4
 
0.4%
Other values (3) 8
 
0.8%
ValueCountFrequency (%)
1 94
9.7%
2 234
24.2%
3 180
18.6%
4 184
19.0%
5 105
10.8%
6 91
 
9.4%
7 43
 
4.4%
8 15
 
1.5%
9 10
 
1.0%
10 4
 
0.4%
ValueCountFrequency (%)
14 1
 
0.1%
12 3
 
0.3%
11 4
 
0.4%
10 4
 
0.4%
9 10
 
1.0%
8 15
 
1.5%
7 43
 
4.4%
6 91
9.4%
5 105
10.8%
4 184
19.0%
Distinct951
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-15T16:36:45.397208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length56
Median length39.5
Mean length15.214876
Min length0

Characters and Unicode

Total characters14728
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique935 ?
Unique (%)96.6%

Sample

1st rowabc
2nd rowlet
3rd row want you back
4th rowcecilia
5th rowspirit sky
ValueCountFrequency (%)
you 126
 
5.0%
love 114
 
4.5%
get 26
 
1.0%
up 18
 
0.7%
just 18
 
0.7%
like 18
 
0.7%
woman 17
 
0.7%
way 17
 
0.7%
night 17
 
0.7%
baby 17
 
0.7%
Other values (1076) 2143
84.7%
2024-09-15T16:36:45.923248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2586
17.6%
e 1437
 
9.8%
o 1137
 
7.7%
a 897
 
6.1%
n 863
 
5.9%
l 754
 
5.1%
t 717
 
4.9%
i 710
 
4.8%
r 637
 
4.3%
s 618
 
4.2%
Other values (40) 4372
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2586
17.6%
e 1437
 
9.8%
o 1137
 
7.7%
a 897
 
6.1%
n 863
 
5.9%
l 754
 
5.1%
t 717
 
4.9%
i 710
 
4.8%
r 637
 
4.3%
s 618
 
4.2%
Other values (40) 4372
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2586
17.6%
e 1437
 
9.8%
o 1137
 
7.7%
a 897
 
6.1%
n 863
 
5.9%
l 754
 
5.1%
t 717
 
4.9%
i 710
 
4.8%
r 637
 
4.3%
s 618
 
4.2%
Other values (40) 4372
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2586
17.6%
e 1437
 
9.8%
o 1137
 
7.7%
a 897
 
6.1%
n 863
 
5.9%
l 754
 
5.1%
t 717
 
4.9%
i 710
 
4.8%
r 637
 
4.3%
s 618
 
4.2%
Other values (40) 4372
29.7%

Interactions

2024-09-15T16:36:37.200048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:35:54.540067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:35:57.417232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:35:59.035144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:00.457226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:02.232263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:04.122133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:08.837081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:14.080494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:18.618007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:23.143804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:27.675911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:32.531879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:35.699847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:35:54.664354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:18.897904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:27.989767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:34.405224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:35:57.601136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:28.300454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:19.759004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:28.959007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:32.886162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:34.654487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:37.622100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:35:59.491272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:01.318404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:10.296592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:20.051487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:24.625180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:29.289974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:32.976240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:34.742177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:24.939893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:16.223416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:25.233450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-15T16:36:02.043319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:03.533947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:08.201566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:12.756562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:17.995629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:22.538693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:27.011223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:31.890588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:34.147138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:35.526959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:36.979684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:38.398111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:35:57.318217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:35:58.935347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:00.343079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:02.143460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:03.805961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:08.517407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:13.771951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:18.307786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:22.836784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:27.314455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:32.202904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:34.231751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:35.613451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T16:36:37.065866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-15T16:36:46.073959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AcousticnessDanceabilityDurationEnergyInstrumentalnessKeyLivenessLoudnessModePopularitySpeechinessTempoTime_SignatureTrack_LengthTrack_Word_LengthValenceYear
Acousticness1.000-0.249-0.168-0.562-0.0350.0380.057-0.3690.139-0.119-0.187-0.1050.1850.1430.138-0.210-0.115
Danceability-0.2491.000-0.0140.207-0.0140.008-0.2250.0730.0910.0950.232-0.1130.143-0.115-0.1160.5290.121
Duration-0.168-0.0141.0000.0250.1800.053-0.047-0.0470.0140.125-0.041-0.0270.000-0.021-0.039-0.0620.162
Energy-0.5620.2070.0251.0000.002-0.0500.0580.6540.0650.0790.3280.1470.171-0.092-0.1060.3940.007
Instrumentalness-0.035-0.0140.1800.0021.0000.030-0.080-0.1770.1050.004-0.079-0.0530.117-0.012-0.023-0.006-0.008
Key0.0380.0080.053-0.0500.0301.0000.069-0.0740.205-0.043-0.001-0.0180.0000.0460.052-0.019-0.041
Liveness0.057-0.225-0.0470.058-0.0800.0691.0000.0790.025-0.0270.025-0.0200.0190.0290.028-0.141-0.008
Loudness-0.3690.073-0.0470.654-0.177-0.0740.0791.0000.0000.1610.1540.0670.414-0.149-0.1350.0310.035
Mode0.1390.0910.0140.0650.1050.2050.0250.0001.0000.0830.0420.0000.0510.0000.0330.0000.066
Popularity-0.1190.0950.1250.0790.004-0.043-0.0270.1610.0831.0000.0230.0210.053-0.404-0.352-0.0310.136
Speechiness-0.1870.232-0.0410.328-0.079-0.0010.0250.1540.0420.0231.0000.1270.000-0.145-0.1310.092-0.055
Tempo-0.105-0.113-0.0270.147-0.053-0.018-0.0200.0670.0000.0210.1271.0000.126-0.053-0.0570.0660.033
Time_Signature0.1850.1430.0000.1710.1170.0000.0190.4140.0510.0530.0000.1261.0000.0000.0000.1690.035
Track_Length0.143-0.115-0.021-0.092-0.0120.0460.029-0.1490.000-0.404-0.145-0.0530.0001.0000.9170.041-0.064
Track_Word_Length0.138-0.116-0.039-0.106-0.0230.0520.028-0.1350.033-0.352-0.131-0.0570.0000.9171.0000.015-0.038
Valence-0.2100.529-0.0620.394-0.006-0.019-0.1410.0310.000-0.0310.0920.0660.1690.0410.0151.000-0.034
Year-0.1150.1210.1620.007-0.008-0.041-0.0080.0350.0660.136-0.0550.0330.035-0.064-0.038-0.0341.000

Missing values

2024-09-15T16:36:38.542215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T16:36:38.770960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearTrack_LengthTrack_Word_LengthTrack Transformed
0abcThe Jackson 516240.6820.9263-2.51500.06070.0404000.0000000.19000.860105.96981197031abc
1let it beThe Beatles24340.4430.4030-8.33910.03220.6310000.0000000.11100.410143.46278197093let
2i want you backThe Jackson 517640.4690.5388-13.55910.05750.3050000.0001140.37000.885196.606781970154want you back
3ceciliaSimon & Garfunkel17440.7550.8760-8.86710.03620.3570000.0000050.22000.954102.76276197071cecilia
4spirit in the skyNorman Greenbaum24240.6090.6179-7.09110.03070.0994000.0040400.11800.543128.903751970174spirit sky
5love grows (where my rosemary goes)Edison Lighthouse17440.5680.8249-4.61310.02990.4030000.0000000.08550.753108.625731970356love grows where rosemary goes
6the letterJoe Cocker9140.7210.4968-6.29610.06450.1400000.0001010.10200.18081.499721970102letter
7the house of the rising sunFrijid Pink27130.2950.5849-6.69600.03450.0003850.2180000.09960.228117.200711970276house rising sun
8fire and rainJames Taylor20340.5970.2715-17.29310.03940.7660000.0119000.09330.33876.271711970133fire rain
9in the summertimeMungo Jerry21140.7540.4494-14.01310.06150.7240000.0000000.16200.97382.751711970173summertime
TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearTrack_LengthTrack_Word_LengthTrack Transformed
969a little more loveOlivia Newton-John20740.7170.4148-14.85510.03640.029000.0094400.08650.494100.178391979184little more love
970in the navyVillage People22540.7590.8897-10.59200.05020.125000.0000000.04100.886126.201381979113navy
971mama can’t buy you loveElton John24440.5290.4325-14.24510.03330.524000.0000000.11500.55594.382361979235mama can’t buy you love
972goodnight tonightPaul McCartney & Wings26040.7480.6831-9.88500.04660.056600.0006390.08090.943123.385351979172goodnight tonight
973we’ve got tonightBob Seger & The Silver Bullet Band21540.3790.3878-9.28310.02780.757000.0000000.10300.22261.530261979173got tonight
975he’s the greatest dancerSister Sledge37540.7000.8157-9.71100.04400.001150.0012400.09010.837113.245141979244greatest dancer
976don’t cry out loudMelissa Manchester13540.2980.2520-8.95010.03390.901000.0000090.12700.19390.95591979184cry out loud
977when you’re in love with a beautiful womanDr. Hook17440.6650.6638-11.36710.03860.485000.0068200.15700.792110.65671979428love beautiful woman
978i’ll never love this way againDionne Warwick17840.4520.4348-8.87010.03990.792000.0139000.16500.247137.70251979306never love way again
979dim all the nightsDonna Summer24840.7580.5407-10.91110.03850.055100.0000000.03430.661121.58101979184dim all nights